Research suggests that starting a day with a bad news does not have a good impact on readers. This has introduced the need of creating an application that can let a reader know if the news is good or bad. This project presents and evaluates a classification approach using news articles from a major English-language newspaper published in Nepal. In this project, a system has been built for the prediction of sentiment of news articles. In order to extract news articles, web crawler, porter’s algorithm, logistic regression have been used. Similarly, machine learning has been used for news classification purpose. This machine learning approach classifies a news article by analyzing its headline. An authentic site has been chosen in order to implement these classifications. For the training purpose, 2000 datasets have been used. Similarly, for the test purpose, 200 datasets have been used. The test showed the accuracy of 73% and 80% in the news classification using Logistic Regression and Naïve Bayes Classification respectively. Upon knowing the nature of the news, a reader can go through the content simply by clicking on the link that has been displayed along with an emoticon that specifies the nature. Despite expectations from the application to have a good impact on people, this project cannot cover all of the news sites. However, it is expected to help the public get updates such that it does not carry any negative impact on their health.